A computer vision system for automatic steel surface inspection

Yung Chun Liu, Yu Lu Hsu, Yung Nien Sun, Song Jan Tsai, Chiu Yi Ho, Chung Mei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

Automatic inspection on line plays an important role in industrial quality management nowadays. This paper proposes a new computer vision system for automatic steel surface inspection. The system analyzes the images sequentially acquired from steel bar to detect different kinds of defects on the steel surface. Several image processing strategies are used to detect and outline the defects. The detected defects are then classified into different defect types by using a hierarchical neural network classifier. Some manual detection results by field experts are used to verify the correctness of the proposed detection. In defect classification, the results show that the relevance vector machine (RVM) has better accuracy than the back propagation neural network (BPN). The proposed algorithm was found capable of detecting defects on steel surface rapidly and precisely.

Original languageEnglish
Title of host publicationProceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
Pages1667-1670
Number of pages4
DOIs
Publication statusPublished - 2010
Event5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 - Taichung, Taiwan
Duration: 2010 Jun 152010 Jun 17

Publication series

NameProceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010

Other

Other5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
CountryTaiwan
CityTaichung
Period10-06-1510-06-17

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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